1.0 Field of the Disclosure
The present disclosure relates to a process, computer program product and system of image processing to identify, map and/or classify damage of geographic areas and, more particularly, the present disclosure relates to a process, computer program product and system of image processing using wavelet transformation to identify, map and/or classify damage of geographic areas, such as damage caused by, e.g., earthquakes, hurricanes, tornadoes, floods and the like.
2.0 Related Art
Hurricanes on average make landfall on U.S. soil 1.2 (for El Niño years) to 2.1 times per year (for La Niña years) resulting in normalized mean damage of $7.7 (El Niño years) to $9.2 billion (La Niña years) per year. Since the 1990s tornadoes have impacted the U.S. approximately 1,000 times per year causing severe localized damage. These disasters raise immediate questions about the extent and severity of damage, which can be answered by image-based damage assessments to aid in the response and recovery phases. Image-based damage assessments can meet the response and recovery information needs only if they are collected soon after the event and over the correct location, contain sufficient geographic detail, and are evaluated by skilled interpreters. The National Academy's Committee on Planning for Catastrophe has suggested that processes which can reduce the time between image acquisition and delivery to responders to twenty-four to forty-eight hours are of particular interest.
Such a methodology with an analytical component can also be used by the United States Federal Courts, which according to the Daubert decision are required to consider several factors when evaluating the suitability of scientific evidence, including the scientific validity, acceptability and accuracy of the methodology, and acceptability of the methodology. An algorithmic methodology would more easily allow an analytical assessment of offered damage assessment evidence against the Daubert falsifiability, and potential error rates factors.
The Federal Emergency Management Agency (FEMA) currently produces image-based damage assessments (FIG. 1 is an example from hurricane Katrina) using skilled visual interpretations of post event imagery. These damage assessments have been performed by a varied number of interpreters working with an evolving set of rules, requirements and tools. For example, NOAA collected post hurricane Camille imagery long before now commonly available remote sensing, geographic information systems (GIS) and global positioning systems (GPS) tools became available. Hurricane Andrew in 1993, the World Trade Center attack in September 2001, and hurricane Katrina in August 2005, accelerated the collection and use of high resolution aerial imagery for response, recovery and study of U.S. disasters. For example, NOAA collected and posted online over 3,000 post hurricanes Isabel, Ivan and Jeanne images for immediate damage assessment and long-term research.
There are several potential issues with a visual interpretation based damage assessment approach that may be addressed by use of an algorithmic image damage assessment approach. Rapid response visual interpretation of overhead imagery requires significant human and infrastructure resources. Hurricanes typically cause wide areas of damage resulting in large volumes of imagery. Many skilled interpreters working concurrently on this large volume of imagery requires access to at least a workstation with image processing and geographic information system (GIS) software running on relatively robust, multi-monitor workstations. Second, damaging hurricanes make landfall at unpredictable times and with uncertain periodicity. This sporadic and unpredictable nature of hurricane landfall hinders the development and maintenance of a dedicated, trained, ongoing human capability for visual damage assessment. The result is that visual interpretation teams are pulled together at the last minute from wherever they are available. This ad hoc human capability makes it difficult for damage assessments to be performed consistently across large storms and from storm to storm. Fourth, a human visual interpreter based method for damage assessment is not easily calibrated. The research herein addresses these damage assessment issues with a methodology for algorithmic damage assessment using overhead imagery informed by a wavelet transform based approach.
Moreover, past systems and techniques have been slow (often providing results in terms of days or weeks) and at times inaccurate to identify and assess damage to target areas. Often human interpretation is required of current type systems that may lead to inaccurate assessments.
Therefore, a method and system to provide fast analysis, identification and categorization of damage due to natural or man-made events such as hurricanes, tornados, floods, earthquakes which provides accurate damage information within a short time period measured in hours or less would greatly aid identifying and assessing where and how severe damage has occurred, and also to improve recovery efforts of many kinds.